Archive for the ‘Blogroll’ Category

By Tasso Argyros in Analytics, Blogroll, Data-Analytics Server on September 15, 2010

In the recently announced nCluster 4.6 we continue to innovate and improve nCluster on many fronts to make it the high performance platform of choice for deep, high value analytics. One of the new features is a hybrid data store, which now gives nCluster users the option of storing their data in either a row or column orientation. With the addition of this feature, nCluster is the first data warehouse and analytics platform to combine a tightly integrated hybrid row- and column-based storage with SQL-MapReduce processing capabilities. In this post we’ll discuss the technical details of the new hybrid store as well as the nCluster customer workloads that prompted the design.

Row- and Column-store Hybrid

Let’s start with the basics of row and column stores. In a row store, all of the attribute values for a particular record are stored together in the same on-disk page. Put another way, each page contains one or more entire records. Such a layout is the canonical database design found in most database textbooks, as well as both open source and commercial databases. A column store flips this model around and stores values for only one attribute on each on-disk page. This means that to construct, say, an entire two-attribute record will require data from two different pages in a column store, whereas in a row-store the entire record would be found on only one page. If a query needs only one attribute in that same two-attribute table, then the column store will deliver more needed values per page read. The row store must read pages containing both attributes even though only one attribute is needed, wasting some I/O bandwidth on the unused attribute. Research has shown that for workloads where a small percentage of attributes in a table are required, a column oriented storage model can result in much more efficient I/O because only the required data is read from disk. As more attributes are used, a column store becomes less competitive with a row store because there is an overhead associated with combining the separate attribute values into complete records. In fact, for queries that access many (or all!) attributes of a table, a column store performs worse and is the wrong choice. Having a hybrid store provides the ability to choose the optimal storage for a given query workload.

Aster Data customers have a wide range of analytics use cases from simple reporting to advanced analytics such as fraud detection, data mining, and time series analysis. Reports typically ask relatively simple questions of data such as total sales per region or per month. Such queries tend to require only a few attributes and therefore benefit from columnar storage. In contrast, deeper analytics such as applying a fraud detection model to a large table of customer behaviors relies on applying that model to many attributes across many rows of data. In that case, a row store makes a lot more sense.

Clearly there are cases where having both a column and row store benefits an analytics workload, which is why we have added the hybrid data store feature to nCluster 4.6.

Performance Observations

What does the addition of a hybrid store mean for typical nCluster workloads? The performance improvements from reduced I/O can be considerable: a 5x to 15x speedup was typical in some in-house tests on reporting queries. These queries were generally simple reporting queries with a few joins and aggregation. Performance improvement on more complex analytics workloads, however, was highly variable, so we took a closer look at why. As one would expect (and a number of columnar publications demonstrate), we also find that queries that use all or almost all attributes in a table benefit little or are slowed down by columnar storage. Deep analytical queries in nCluster like scoring, fraud detection, and time series analysis tend to use a higher percentage of columns. Therefore, as a class, they did not benefit as much from columnar, but when these queries do use a smaller percentage of columns, choosing the columnar option in the hybrid store provided good speedup.

A further reason that these more complex queries benefit less from a columnar approach is Amdahl’s law. As we push more complex applications into the database via SQL-MapReduce, we see a higher percentage of query time spent running application code rather than reading or writing from disk. This highlights an important trend in data analytics: user CPU cycles per byte is increasing, which is one reason that deployed nCluster nodes tend to have a higher CPU per byte ratio than one might expect in a data warehouse. The takeaway message is that the hybrid store provides an important performance benefit for simple reporting queries and for analytical workloads that include a mix of ad hoc and simple reporting queries, performance is maximized by choosing the data orientation that is best suited for each workload.


The hybrid store is made possible by integrating a column store within the nCluster data storage and query-processing engine, which already used row-storage. The new column storage is tightly integrated with existing query processing and system services. This means that any query answerable by the existing Aster storage engine can now also be answered in our hybrid store, whether the data is stored in row or column orientation. Moreover, all SQL-MapReduce features, workload management, replication, fail-over, and cluster backup features are available to any data stored in the hybrid store.

Providing flexibility and high performance on a wide range of workloads, makes Aster Data the best platform for high value analytics. To that end, we look forward to continuing development of the nCluster hybrid storage engine to further optimize row and column data access. Coupled with workload management and SQL-MapReduce, the new hybrid nCluster storage highlights Aster Data’s commitment to provide nCluster users with the most flexibility to make the most of their data.

By Tasso Argyros in Analytics, Blogroll on August 10, 2010

Coming out of Stanford to start Aster Data five years back, my co-founders and I had to answer a lot of questions. What kind of an engineering team do we want to build? Do we want people experienced in systems or databases? Do we want to hire people from Oracle or another established organization? When you’re just starting a company, embarking on a journey that you know will have many turns, answers are not obvious.

What we ended up doing very early on is bet on intelligent, smart and adaptable engineers, as opposed to experience or a long resume. It turned out that this was the right thing to do because, as a startup, we had react to market needs and change our focus at a blink of an eye. Having a team of people that were used to tackling never-seen-before problems made us super-agile as a product organization. As the company grew, we ended up having a mix of people that combined expertise in certain areas and core engineering talent. But the culture of the company was set in stone even though we didn’t realize it: even today our interview process expects talent, intelligence and flexibility to be there and strongly complement the experience our candidates may have.

There are three things that are great about being an engineer at Aster Data:

Our Technology Stack is Really Tall.

We have people working right above the Kernel on filesystems, workload management, I/O performance, etc. We have many challenging problems that involve very large scale distributed systems - and I’m talking about the whole nine yards, including performance, reliability, manageability, and data management at scale. We have people working on database algorithms from the I/O stack to the SQL planner to no-SQL planners. And we have a team of people working on data mining and statistical algorithms on distributed systems (this is our “quant”? group since people there come with a background in physics as much as computer science). It’s really hard to get bored or stop learning here.

We Build Real Enterprise Software.

There’s a difference between the software one would write in a company like Aster Data versus a company like Facebook. Both companies write software for big data analysis. However, a company like Facebook solves their problem (a very big problem, indeed) for themselves and each engineer gets to work on a small piece of the pie. At Aster Data we write software for enterprises and due to our relatively small size each engineer makes a world of a difference. We also ship software to third-party people and they expect our software to be out-of-the-box resilient, reliable and easy to manage/debug. This makes the problem more challenging but also gives us great leverage: once we get something right, not one, nor two, but potentially hundreds or thousands of companies can benefit from our products. The impact of the work of each engineer at Aster Data is truly significant.

We’re Working on (Perhaps) the Biggest IT Revolution of the 21st Century.

Big Data. Analytics. Insights. Data Intelligence. Commodity hardware. Cloud/elastic data management. You name it. We have it. When we started Aster Data in 2005 we just wanted to help corporations analyze the mountains of data that they generate. We thought it was a critical problem for corporations if they wanted to remain competitive and profitable. But the size and importance of data grew beyond anyone’s expectations over the past few years. We can probably thank Google, Facebook and the other internet companies for demonstrating to the world what data analytics can do. Given the importance and impact of our work, there’s no ceiling on how successful we can become.

You’ve probably guessed it by now, but the reason I’m telling you all this is to also tell you that we’re hiring. If you think you have what it takes to join such an environment, I’d encourage you to apply. We get many applications daily so the best way to get an interview here is through a recommendation and referral. With tools like LinkedIn (who happens to be a customer) it’s really easy to explore your network. My LinkedIn profile is here, so see if we have a professional or academic connection. You can also look at our management team, board of directors, investors and advisors to see if there are any connections there. If there’s no common connection, feel free to email your resume to However, to stand out I’d encourage you to tell us a couple of words about what excites you about Aster Data, large scale distributed systems, databases, analytics and/or startups that work to revolutionize an industry, and why you think you’ll be successful here. Finally, take a look at the events we either organize or participate in - it’s a great way to meet someone from our team and explain why you’re excited to join our quest to revolutionize data management and analytics.

By Tasso Argyros in Blogroll on July 19, 2010

Every year or so Google comes out with an interesting piece of infrastructure, always backed by claims that it’s being used by thousands of people on thousands of servers and processes petabytes or exabytes of web data. That alone makes Google papers interesting reading. :)

This latest piece of research just came out on Google’s Research Buzz page. It’s about a system called Dremel (note: Dremel is a company building hardware tools which I happened to use a lot when I was building model R/C airplanes as a kid). Dremel is an interesting move by Google which provides a system for interactive analysis of data. It was created because it was thought that native MapReduce has too much latency for for fast interactive querying/analysis. It uses data that sits on different storage systems like GFS or BigTable. Data is modeled in a columnar, semi-structured format and the query language is SQL-like with extensions to handle the non-relational data model. I find this interesting - below is my analysis of what Dremel is and the big conclusion.

Main characteristics of the system:

Data & Storage Model

– Data is stored in a semi-structured format. This is not XML, rather it uses Google’s Protocol Buffers. Protocol Buffers (PB) allow developers to define schemas that are nested.
– Every field is stored in its own file, i.e. every element of the Protocol Buffers schema is columnar-ized.

Columnar modeling is especially important for Dremel for two specific reasons:

– Protocol Buffer data structures can be huge (> 1000 fields).
– Dremel does not offer any data modeling tools to help break these data structures down. E.g. there’s nothing in the paper that explains how you can take a Protocol Buffers data structure and break it down to 5 different tables.
– Data is stored in a way that makes it possible to recreate the orignial flat? schema from the columnar representation. This however requires a full pass over the data - the paper doesn’t explain how point or indexed queries would be executed.
– There’s almost no information about how data gets in the right format, how is it stored, deleted, replicated, etc. My best guess is that when someone defines a Dremel table, data is copied from the underlying storage to the local storage of Dremel nodes (leaf nodes?) and at the same time is replicated across the leaf nodes. Since data in Dremel cannot be updated (it seems to be a write-once-read-many model), design & implementation of the replication subsystem should be significantly simplified.


Query interface is SQL-like but with extensions to handle the semi-structured, nested nature of data. Input of queries is semi-structured, and output is semi-structured as well. One needs to get used to this since it’s significantly different from the relational model.
– Tables can be defined from files, e.g. stored in GFS by means of a DEFINE TABLE? command.
The data model and query language makes Dremel appropriate for developers; for Dremel to be used by analysts or database folks, a different/simpler data model and a good number of tools (for loading, changing the data model etc) would be needed.

Query Execution

Queries do NOT use MapReduce, unlike Hadoop query tools like Pig & Hive.
– Dremel provides optimizations for sequential data access, such as async I/O & prefetching.
– Dremel supports approximate results (e.g. return partial results after reading X% of data - this speeds up processing in systems with 100s of servers or more since you don’t have to wait for laggards).
– Dremel can use replicas to speed up execution if a server becomes too slow. This is similar to the “backup copies”? idea from the original Google MapReduce paper.
There seems to be a tree-like model of executing queries, meaning that there are intermediate layers of servers between the leaf nodes and the top node (which receives the user query). This is useful for very large deployments (e.g. thousands of servers) since it provides some intermediate aggregation points that reduce the amount of data that needs to flow to any single node.

Performance & Scale

Compared to Google’s native MapReduce implementation, Dremel is two orders of magnitude faster in terms of query latency. As mentioned above, part of the reason is that the Protocol Buffers are usually very large and Dremel doesn’t have a way to break those down except for its columnar modeling. Another reason is the high startup cost of Google’s MapReduce implementation.
– Following Google’s tradition, Dremel was shown to scale reasonably well to thousands of servers although this was demonstrated only over a single query that parallelizes nicely and from what I understand doesn’t reshuffle much data. To really understand scalability, it’d be interesting to see benchmarks with a more complex workload collection.
– The paper mentions little to nothing about how data is partitioned across the cluster. Scalability of the system will probably be sensitive to partitioning strategies, so that seems like a significant omission IMO.

So the big question: Can MapReduce itself handle fast, interactive querying?

– There’s a difference between the MapReduce paradigm, as an interface for writing parallel applications, and a MapReduce implementation (two examples are Google’s own MapReduce implementation, which is mentioned in the Dremel paper, and open-source Hadoop). MapReduce implementations have unique performance characteristics.
– It is well known that Google’s MapReduce implementation & Hadoop’s MapReduce implementation are optimized for batch processing and not fast, interactive analysis. Besides the Dremel paper, look at this Berkeley paper for some Hadoop numbers and an effort to improve the situation.
Native MapReduce execution is not fundamentally slow; however Google’s MapReduce and Hadoop happen to be oriented more towards batch processing. Dremel tries to overcome that by building a completely different system that speeds interactive querying. Interestingly, Aster Data’s SQL-MapReduce came about to address this in the first place and offers very fast interactive queries even though it uses MapReduce. So the idea that one needs to get rid of MapReduce to achieve fast interactivity is something I disagree with - we’ve shown this is not the case with SQL-MapReduce.

By Tasso Argyros in Analytics, Blogroll on July 12, 2010

I have always enjoyed the subtle irony of someone trying to be impressive by saying “my data warehouse is X Terabytes”? [muted: "and it's bigger than yours"?]! Why is this ironic? Because it describes a data warehouse, which is supposed to be all about data processing and analysis, using a storage metric. Having an obese 800 Terabytes system that may take hours or days to just do a single pass over the data is not impressive and definitely calls for some diet.

Surprisingly though, several vendors went down the path of making their data warehousing offerings fatter and fatter. Greenplum is a good example. Prior to Sun’s acquisition by Oracle, they were heavily pushing systems based on the Sun Thumper, a 48-disk-heavy 4U box that can store up to 100TBs/box. I was quite familiar with that box as it partly came out of a startup called Kealia that my Stanford advisor, David Cheriton, and Sun co-founder Andy Bechtolsheim had founded and then sold to Sun in 2004. I kept wondering, though, what a 50TB/CPU configuration has to do with data analytics.

After long deliberation I came to the conclusion that it has nothing to do with it. There were two reasons why people were interested in this configuration. First, there were some use cases that required “near-line storage”?, a term that’s used to describe a data repository whose major purpose is to store data but also allows for basic & infrequent data access. In that respect, Greenplum’s software on top of the Sun Thumpers represented a cheap storage solution that offered basic data access and was very useful for applications where processing or analytics was not the main focus.

The second reason for the interest, though, is a tendency to drive DW projects towards an absolute low per-TB price to reduce costs. Experienced folks will recognize that such an approach leads to disaster, because (as mentioned above) analytics is more than just Terabytes. Perfectly low per-TB price using fat storage looks great on glossy paper but in reality it’s no good because nobody’s analytical problems are that simple.

The point here is that analytics have more to do with processing rather than storage. It requires a fair number of balanced servers (thus good scalability & fault tolerance), CPU cycles, networking bandwidth, smart & efficient algorithms, fair amounts of memory to avoid thrashing etc. It’s also about how much processing can it be done by SQL, and how much of your analytics need to use next-generation interfaces like MapReduce or pre-packaged in-database analytical engines. In the new decade in which we’re embarking, solving business problems like fraud, market segmentation & targeting, financial optimization, etc., require much more than just cheap, overweight storage.

So going to the EMC/Greenplum news, I think such an acquisition makes sense, but in a specific way. It will lead to systems that live between storage and data warehousing, systems able to store data and also give the ability to retrieve it on an occasional basis or if the analysis required is trivial. But the problems Aster is excited about are those of advanced in-database analytics for rich, ad hoc querying, delivered through a full application environment inside a MPP database. It’s these problems that we see as opportunities to not only cut IT costs but also provide tremendous competitive advantages to our customers. And on that front, we promise to continue innovating and pushing the limits of technology as much as possible.

By Tasso Argyros in Blogroll on July 11, 2010

Those of you that follow the academic conferences in the database space, are probably familiar with EDBT, the premier database conference in Europe. EDBT acts as a forum not only for European researchers, but also for commercial technologies and vendors that want to present their innovations in a European setting.

For 2011, EDBT is held in Uppsala, Sweden in March. I’m on the Program Committee for the “industrial application”? section and I’d like to encourage anyone with an interesting commercial technology and an interest in Europe to consider submitting a paper to the conference. Papers on applications and position papers on technology trends are equally welcome. The deadline for submission is September 8, 2010 and you can find more info on submitting here.

By Tasso Argyros in Analytics, Blogroll on July 2, 2010

There is a lot of talk these days about relational vs. non-relational data. But what about analytics? Does it make sense to talk about relational and non-relational analytics?

I think it does. Historically, a lot of data analysis in the enterprise has been done with pure SQL. SQL-based analysis is a type of “relational analysis,”? which I define as analysis done via a set-based declarative language like SQL. Note how SQL treats every table as a set of values; SQL statements are relational set operations; and any intermediate SQL results, even within the same query, need to follow the relational model. All these are characteristics of a relational analysis language. Although recent SQL standards define the language to be Turing Complete, meaning you can implement any algorithm in SQL, in practice implementing any computation that departs from the simple model of sets, joins, groupings, and orderings is severely sub-optimal, in terms of performance or complexity.

On the other hand, an interface like MapReduce is clearly non-relational in terms of its algorithmic and computational capabilities. You have the full flexibility of a procedural programming language, like C or Java; MapReduce intermediate results can follow any form; and the logic of a MapReduce analytical application can implement almost arbitrary formations of code flow and data structures. In addition, any MapReduce computation can be automatically extended to a shared-nothing parallel system which implies ability to crunch big amounts of data. So MapReduce is one version of “non-relational”? analysis.

So Aster Data’s SQL-MapReduce becomes really interesting if you see it as a way of doing non-relational analytics on top of relational data. In Aster Data’s platform, you can store your data in a purely relational form. By doing that, you can use popular RDBMS mechanisms to achieve things like adherence to a data model, security, compliance, integration with ETL or BI tools etc. The similarities, however, stop there. Because you can then use SQL-MapReduce to do analytics that were never possible before in a relational RDBMS, because they are MapReduce-based and non-relational and they extend to TBs or PBs. And that includes a large number of analytical applications like fraud detection, network analysis, graph algorithms, data mining, etc.

By Tasso Argyros in Analytics, Blogroll, Data-Analytics Server on June 23, 2010

Recently, a journalist called to ask about in-memory data processing, a very interesting subject. I always thought that in-memory processing will be more and more important as memory prices keep falling drastically. In fact, these days you can get 128GB of memory into a single system for less than $5K plus the server cost, not to mention that DDR3 and multiple memory controllers are giving a huge performance boost. And if you run software that can handle shared-nothing parallelism (MPP), your memory cost increases linearly, and systems with TBs of memory are possible.

So what do you do with all that memory? There are two classes of use cases that are emerging today. First is the case where you need to increase concurrent access to data with reduced latency. Tools like memcached offer in-memory caching that, used properly, can vastly improve latency and concurrency for large-scale OLTP applications like websites. Also the nice thing with object caching is that it scales well in a distributed way and people have build TB-level caches. Memory-only OLTP databases have started to emerge, such as VoltDB. And memory is used implicitly as a very important caching layer in open-source key-value products like Voldemort. We should only expect memory to play a more and more important role here.

The second way to use memory is to gain “processing flexibility” when doing analytics. The idea is to throw your data into memory (however much it fits, of course) without spending much time thinking how to do that or what queries you’ll need to run. Because memory is so fast, most simple queries will be executed at interactive times and also concurrency is handled well. European upstart QlikView exploits this fact to offer a memory-only BI solution which provides simple and fast BI reporting. The downside is its applicability to only 10s of GBs of data as Curt Monash notes.

By exploiting an MPP shared-nothing architecture, Aster Data has production clusters with TBs of total memory. Our software takes advantage of memory in two ways: first, it uses caching aggressively to ensure the most relevant data stays in memory; and when data is in memory, processing is much faster and more flexible. Secondly, MapReduce is a great way to utilize memory as it provides full flexibility to the programmer to use memory-focused data structures for data processing. In addition, Aster Data’s SQL-MapReduce provides tools to the user to encourage the development of memory-only MapReduce applications.

However, one shouldn’t fall into the trap of thinking that all analytics will be in-memory anytime soon. While memory is down to $30/GB, disk manufacturers have been busy increasing platter density and dropping their price to less than $0.06/GB. Given that the amount of data in the world grows faster than Moore’s law and memory, there will always be more data to be stored and analyzed than what fits into any amount of memory that an enterprise can use. In fact, most big data applications will have data sets that do not fit into memory because, while tools like memcached worry only about the present (e.g. current Facebook users), analytics need to worry about the past, as well - and that means much more data. So a multi-layer architecture will be the only cost-effective way of analyzing large amounts of data for some time.

One shouldn’t be discussing memory without mentioning solid-state disk products (like Aster Data partner company Fusion-io). SSDs are likely to make the surprise here given that their per-GB price is falling faster than disks (being a solid-state product that follows Moore’s law does help). In the next few years we’ll witness SSDs in read-intensive applications providing similar advantages to memory while accommodating much larger data sizes.

By Tasso Argyros in Blogroll, Data-Analytics Server on June 22, 2010

Rumors abound that Intel is “baking”? the successor of the very successful Nehalem CPU architecture, codenamed Westmere. It comes with an impressive spec: 10 CPU cores (supporting 20 concurrent threads) packed in a single chip. You can soon expect to see 40 cores in middle range 4-socket servers - a number hard to imagine just five years ago.

We’re definitely talking about a different era. In the old days, you could barely fit a single core in a chip. (I still remember 15 years ago when I had to buy and install a separate math co-processor on my Mac LC to run Microsoft Excel and Mathematica.) And with the hardware, software has to change, too. In fact, modern software means software that can handle parallelism. This is what makes MapReduce such an essential and timely tool for big data applications. MapReduce’s purpose in life is to simplify data and processing parallelism for big data applications. It gives ample freedom to the programmer on how to do things locally; and takes over when data needs to be communicated across processes/cores/servers, thus evaporating a lot of the parallelism complexity.

Once someone designs their software and data to operate in a parallelized environment using MapReduce, gains will come on multiple levels. Not only will MapReduce help your analytical applications scale across a cluster of servers with terabytes of data, it will also exploit the billions of transistors and the 10s of CPU cores inside each server. The best part: the programmer doesn’t need to think about the difference.

As an example, consider this great paper out of Stanford discusses MapReduce implementations of popular Machine Learning algorithms. The Stanford researchers considered MapReduce as a way of “porting”? these algorithms (traditionally implemented to run in a single CPU) to a multi-core architecture. But, of course, the same MapReduce implementations can be used to scale these algorithms across a distributed cluster as well.

Hardware has changed - MPP, shared-nothing, commodity servers, and, of course, multi-core. In this new world MapReduce is software’s response for big data processing. Intel and Westmere have just found an unexpected friend.

By Steve Wooledge in Blogroll on June 14, 2010

As the market around big data heats up, it’s great to see the ecosystem for Hadoop, MapReduce, and massively parallel databases expanding. This includes events for education and networking around big data.

As such, Aster Data is co-sponsoring our first official “unconference” the night before the 2010 Hadoop Summit. It’s called BigDataCamp and will be June 28th at the TechMart from 5:00-9:30PM (adjacent to the Hyatt where Hadoop Summit is taking place). Similar to our ScaleCamp event last year where we heard from companies like LinkedIn and ShareThis and industry practitioners like Chris Wensel (author of Cascading), there will be a lineup of great talks, including hands-on workshops led by Amazon Web Services, Karmasphere, and more. In addition, we’re lucky to have Dave Nielsen as the moderator/organizer of the event as he’s chaired similar unconferences such as CloudCamp, and is an expert at facilitating content and discussions to best fit attendee interest.

It’s very fitting to have the more open/dynamic agenda style of an unconference given the audience will be more of the “analytic scientists” - a title which I’ve seen LinkedIn use when describing the rise in job roles dedicated to tackling big data in companies to tease out insights and develop data-driven products and applications. The analytic scientist-customers I speak with who use Aster Data together with Hadoop challenge the norms and move quickly - not unlike an unconference agenda. I expect a night of free thinking (and free drinks/food), big ideas, and a practical look at emerging technologies and techniques to tackle big data. Best of all, the networking portion is a great chance to meet folks to hear what they’re up to and exchange ideas.

Check out the agenda at and note that seats are limited and we expect to sell out, so please REGISTER NOW. Hope to see you there!

Get Smart on MapReduce with
By Tasso Argyros in Analytics, Blogroll on April 16, 2010

This Monday we announced a new web destination for MapReduce, At a high level, this site is the first consolidated source of information & education around MapReduce, the groundbreaking programming model which is rapidly revolutionizing the way people deal with big data. Our vision is to make this site the one-stop-shop for anyone looking to learn how MapReduce can help analyze large amounts of data.

There were a couple reasons why we thought the world of big data analytics needed a resource like this. First, MapReduce is a relatively new technology and we are constantly getting questions from people in the industry wanting to learn more about it, from basic facts to using MapReduce for complex data analytics at Petabyte scale. By placing our knowledge and references in one public destination, we hope to build a valuable self-serve resource to educate many more people than what we could ever reach directly. In addition, we were motivated by the fact that most MapReduce resources out there focus more on specific implementations of MapReduce, which fragments the available knowledge and reduces its value. In this new effort we hope to create a multi-vendor & multi-tool resource which will benefit anyone interested in MapReduce.

We’re already working with analysts such as Curt Monash, Merv Adrian, Colin White and James Kobielus to syndicate their MapReduce-related posts. Going forward, we expect even more analysts, bloggers, practitioners, vendors, and academics to contribute. If traffic grows like we expect, we may eventually add a community forum to aid in interaction and sharing of knowledge and best practices.

I hope you enjoy surfing this new site! Free to email me for any suggestions as we work to make more useful for you.